Simulation of Game Theory Algorithms

This code implements simulation of game theory algorithms and can serve as a learning program for beginners. In this article, we will introduce the detailed functionality and design principles of this code. First, we will elaborate on the implementation method of game theory algorithms, explaining key concepts such as Nash equilibrium calculation, payoff matrix processing, and strategic decision-making mechanisms. We'll demonstrate how the algorithm handles multi-agent interactions through iterative simulation cycles. Second, we will describe the code structure and components, including main modules for agent behavior modeling, outcome evaluation functions, and visualization utilities, making it easier for beginners to understand and utilize the code. The implementation uses object-oriented design with separate classes for players, game rules, and simulation environments. Finally, we will provide practical examples such as Prisoner's Dilemma simulations and coordination games, complete with code snippets showing parameter configuration and result analysis, to help readers better master and apply the code. Through this article's explanations, readers will not only learn how to use this code for game simulations but also gain deeper knowledge about game theory algorithms. We believe this article will be a valuable resource for beginners, helping them quickly grasp fundamental concepts of game theory algorithms and programming techniques. The code includes comprehensive comments and configuration files for easy customization of game parameters and scenarios.